Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Developer Tools
Moss
Documentation and capabilities reference for Moss semantic search. Use for understanding Moss APIs, SDKs, and integration patterns.
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Documentation and capabilities reference for Moss semantic search. Use for understanding Moss APIs, SDKs, and integration patterns.
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- Tencent SkillHub
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Release facts
- Source
- Tencent SkillHub
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- Indexed source record
- Version
- 1.0.3
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- CoderOMaster
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Documentation
Capabilities
Moss is the real-time semantic search runtime for conversational AI. It delivers sub-10ms lookups and instant index updates that run in the browser, on-device, or in the cloud - wherever your agent lives. Agents can create indexes, embed documents, perform semantic/hybrid searches, and manage document lifecycles without managing infrastructure. The platform handles embedding generation, index persistence, and optional cloud sync - allowing agents to focus on retrieval logic rather than infrastructure.
Index Management
Create Index: Build a new semantic index with documents and embedding model selection Load Index: Load an existing index from persistent storage for querying Get Index: Retrieve metadata about a specific index (document count, model, etc.) List Indexes: Enumerate all indexes under a project Delete Index: Remove an index and all associated data
Document Operations
Add Documents: Insert or upsert documents into an existing index with optional metadata Get Documents: Retrieve stored documents by ID or fetch all documents Delete Documents: Remove specific documents from an index by their IDs
Search & Retrieval
Semantic Search: Query using natural language with vector similarity matching Keyword Search: Use BM25-based keyword matching for exact term lookups Hybrid Search: Blend semantic and keyword search with configurable alpha weighting (Python SDK) Metadata Filtering: Constrain results by document metadata (category, language, tags) Top-K Results: Return configurable number of best-matching documents with scores
Embedding Models
moss-minilm: Fast, lightweight model optimized for edge/offline use (default) moss-mediumlm: Higher accuracy model with reasonable performance for precision-critical use cases
SDK Methods
JavaScriptPythonDescriptioncreateIndex()create_index()Create index with documentsloadIndex()load_index()Load index from storagegetIndex()get_index()Get index metadatalistIndexes()list_indexes()List all indexesdeleteIndex()delete_index()Delete an indexaddDocs()add_docs()Add/upsert documentsgetDocs()get_docs()Retrieve documentsdeleteDocs()delete_docs()Remove documentsquery()query()Semantic / hybrid search
API Actions
All REST API operations go through POST /v1/manage (base URL: https://service.usemoss.dev/v1) with an action field: ActionPurposeExtra required fieldsinitUploadGet a presigned URL to upload index dataindexName, modelId, docCount, dimensionstartBuildTrigger an index build after uploading datajobIdgetJobStatusCheck the status of an async build jobjobIdgetIndexFetch metadata for a single indexindexNamelistIndexesEnumerate every index under the project—deleteIndexRemove an index record and assetsindexNamegetIndexUrlGet download URLs for a built indexindexNameaddDocsUpsert documents into an existing indexindexName, docsdeleteDocsRemove documents by IDindexName, docIdsgetDocsRetrieve stored documents (without embeddings)indexName
Basic Semantic Search Workflow
Initialize MossClient with project credentials Call createIndex() with documents and model options ({ modelId: 'moss-minilm' } in JS; "moss-minilm" string in Python) Call loadIndex() to prepare index for queries Call query() with search text and topK (JS) or QueryOptions(top_k=...) (Python) Process returned documents with scores
Hybrid Search Workflow (Python)
Hybrid blending via alpha is available in the Python SDK via QueryOptions: Create and load index as above Call query() with a QueryOptions object specifying alpha alpha=1.0 = pure semantic, alpha=0.0 = pure keyword, alpha=0.6 = 60/40 blend Default is semantic-heavy for conversational use cases
Document Update Workflow
Initialize client and ensure index exists Call addDocs() with new documents (upserts by default — existing IDs are updated) Call deleteDocs() to remove outdated documents by ID
Voice Agent Context Injection Workflow
This is an opt-in integration pattern for voice agent pipelines — it is not automatic behavior of this skill. Initialize MossClient and load index at agent startup In your application code, call query() on each user message to retrieve relevant context Inject search results into the LLM context before generating a response Respond with knowledge-grounded answer (no tool-calling latency)
Offline-First Search Workflow
Create index with documents using local embedding model Load index from local storage Query runs entirely on-device with sub-10ms latency Optionally sync to cloud for backup and sharing
Voice Agent Frameworks
LiveKit: Context injection into voice agent pipeline with inferedge-moss SDK Pipecat: Pipeline processor via pipecat-moss package that auto-injects retrieval results
Authentication
SDK requires project credentials: MOSS_PROJECT_ID: Project identifier from Moss Portal MOSS_PROJECT_KEY: Project access key from Moss Portal export MOSS_PROJECT_ID=your_project_id export MOSS_PROJECT_KEY=your_project_key REST API requires the following on every request: x-project-key header: project access key x-service-version: v1 header: API version projectId field in the JSON body curl -X POST "https://service.usemoss.dev/v1/manage" \ -H "Content-Type: application/json" \ -H "x-service-version: v1" \ -H "x-project-key: moss_access_key_xxxxx" \ -d '{"action": "listIndexes", "projectId": "project_123"}'
Package Installation
LanguagePackageInstall CommandJavaScript/TypeScript@inferedge/mossnpm install @inferedge/mossPythoninferedge-mosspip install inferedge-mossPipecat Integrationpipecat-mosspip install pipecat-moss
Document Schema
interface DocumentInfo { id: string; // Required: unique identifier text: string; // Required: content to embed and search metadata?: object; // Optional: key-value pairs for filtering }
Query Parameters
ParameterSDKTypeDefaultDescriptionindexNameJS + Pythonstring—Target index name (required)queryJS + Pythonstring—Natural language search text (required)topKJSnumber5Max results to returntop_kPythonint5Max results to returnalphaPython onlyfloat~0.8Hybrid weighting: 0.0=keyword, 1.0=semanticfiltersJS + Pythonobject—Metadata constraints
Model Selection
ModelUse CaseTradeoffmoss-minilmEdge, offline, browser, speed-firstFast, lightweightmoss-mediumlmPrecision-critical, higher accuracySlightly slower
Performance Expectations
Sub-10ms local queries (hardware-dependent) Instant index updates without reindexing entire corpus Sync is optional; compute stays on-device No infrastructure to manage
Chunking Best Practices
Aim for ~200–500 tokens per chunk Overlap 10–20% to preserve context Normalize whitespace and strip boilerplate
Common Errors
ErrorCauseFixUnauthorizedMissing credentialsSet MOSS_PROJECT_ID and MOSS_PROJECT_KEYIndex not foundQuery before createCall createIndex() firstIndex not loadedQuery before loadCall loadIndex() before query()Missing embeddings runtimeInvalid modelUse moss-minilm or moss-mediumlm
Async Pattern
All SDK methods are async — always use await: // JavaScript import { MossClient, DocumentInfo } from '@inferedge/moss' const client = new MossClient(process.env.MOSS_PROJECT_ID!, process.env.MOSS_PROJECT_KEY!) await client.createIndex('faqs', docs, { modelId: 'moss-minilm' }) await client.loadIndex('faqs') const results = await client.query('faqs', 'search text', { topK: 5 }) # Python import os from inferedge_moss import MossClient, QueryOptions client = MossClient(os.getenv('MOSS_PROJECT_ID'), os.getenv('MOSS_PROJECT_KEY')) await client.create_index('faqs', docs, 'moss-minilm') await client.load_index('faqs') results = await client.query('faqs', 'search text', QueryOptions(top_k=5, alpha=0.6)) For additional documentation and navigation, see: https://docs.moss.dev/llms.txt
Category context
Code helpers, APIs, CLIs, browser automation, testing, and developer operations.
Source: Tencent SkillHub
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Package contents
Included in package
1 Docs1 Config
- SKILL.md
- _meta.json